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Journal of Bioinformatics and Computational Biology

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https://www.readbyqxmd.com/read/29739307/introduction-to-papers-selected-from-mccmb-17
#1
Mikhail Gelfand
No abstract text is available yet for this article.
April 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29739306/neanderthal-and-denisovan-ancestry-in-papuans-a-functional-study
#2
Evgeny E Akkuratov, Mikhail S Gelfand, Ekaterina E Khrameeva
Sequencing of complete nuclear genomes of Neanderthal and Denisovan stimulated studies about their relationship with modern humans demonstrating, in particular, that DNA alleles from both Neanderthal and Denisovan genomes are present in genomes of modern humans. The Papuan genome is a unique object because it contains both Neanderthal and Denisovan alleles. Here, we have shown that the Papuan genomes contain different gene functional groups inherited from each of the ancient people. The Papuan genomes demonstrate a relative prevalence of Neanderthal alleles in genes responsible for the regulation of transcription and neurogenesis...
April 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29739305/comparative-analysis-of-protein-coding-and-long-non-coding-transcripts-based-on-rna-sequence-features
#3
Oxana A Volkova, Yury V Kondrakhin, Timur A Kashapov, Ruslan N Sharipov
RNA plays an important role in the intracellular cell life and in the organism in general. Besides the well-established protein coding RNAs (messenger RNAs, mRNAs), long non-coding RNAs (lncRNAs) have gained the attention of recent researchers. Although lncRNAs have been classified as non-coding, some authors reported the presence of corresponding sequences in ribosome profiling data (Ribo-seq). Ribo-seq technology is a powerful experimental tool utilized to characterize RNA translation in cell with focus on initiation (harringtonine, lactimidomycin) and elongation (cycloheximide)...
April 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29495922/boosted-neural-networks-scoring-functions-for-accurate-ligand-docking-and-ranking
#4
Hossam M Ashtawy, Nihar R Mahapatra
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order poses to select the most promising conformation. This BA-based approach is widely applied and some success has been reported, but it is inconsistent and still far from perfect. The main reason for this is that SFs are trained on experimental BA values of only native poses found in co-crystallized structures of protein-ligand complexes (PLCs)...
April 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29495921/relative-sensitivity-analysis-of-the-predictive-properties-of-sloppy-models
#5
Ekaterina Myasnikova, Alexander Spirov
Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e...
April 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29566640/principal-component-analysis-in-protein-tertiary-structure-prediction
#6
Óscar Álvarez, Juan Luis Fernández-Martínez, Celia Fernández-Brillet, Ana Cernea, Zulima Fernández-Muñiz, Andrzej Kloczkowski
We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model...
February 22, 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29566639/integrative-analysis-of-gene-expression-and-dna-methylation-using-unsupervised-feature-extraction-for-detecting-candidate-cancer-biomarkers
#7
Myungjin Moon, Kenta Nakai
Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets...
February 22, 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29591488/prediction-of-microrna-binding-residues-in-protein-using-a-laplacian-support-vector-machine-based-on-sequence-information
#8
Xin Ma, Jing Guo, Xiao Sun
The identification of microRNA (miRNA)-binding protein residues significantly impacts several research areas, including gene regulation and expression. We propose a method, PmiRBR, which combines a novel hybrid feature with the Laplacian support vector machine (LapSVM) algorithm to predict miRNA-binding residues in protein sequences. The hybrid feature is constituted by secondary structure, conservation scores, and a novel feature, which includes evolutionary information combined with the physicochemical properties of amino acids...
February 4, 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29566638/gi-cluster-detecting-genomic-islands-via-consensus-clustering-on-multiple-features
#9
Bingxin Lu, Hon Wai Leong
The accurate detection of genomic islands (GIs) in microbial genomes is important for both evolutionary study and medical research, because GIs may promote genome evolution and contain genes involved in pathogenesis. Various computational methods have been developed to predict GIs over the years. However, most of them cannot make full use of GI-associated features to achieve desirable performance. Additionally, many methods cannot be directly applied to newly sequenced genomes. We develop a new method called GI-Cluster, which provides an effective way to integrate multiple GI-related features via consensus clustering...
February 4, 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29439645/introduction-to-the-9th-young-scientists-school-on-systems-biology-and-bioinformatics-sbb-2017
#10
Yuriy L Orlov, Tatiana V Tatarinova, Maksim V Zakhartsev, Nikolay A Kolchanov
No abstract text is available yet for this article.
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29382254/methymer-design-of-combinations-of-specific-primers-for-bisulfite-sequencing-of-complete-cpg-islands
#11
George S Krasnov, Nataliya V Melnikova, Valentina A Lakunina, Anastasiya V Snezhkina, Anna V Kudryavtseva, Alexey A Dmitriev
We present MethyMer, a Python-based tool aimed at selecting primers for amplification of complete CpG islands. These regions are difficult in terms of selecting appropriate primers because of their low-complexity, high GC content. Moreover, bisulfite treatment, in fact, leads to the reduction of the 4-letter alphabet (ATGC) to 3-letter one (ATG, except for methylated cytosines), and this also reduces region complexity and increases mispriming potential. MethyMer has a flexible scoring system, which optimizes the balance between various characteristics such as nucleotide composition, thermodynamic features (melting temperature, dimers [Formula: see text]G, etc...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29382253/bacterial-promoter-prediction-selection-of-dynamic-and-static-physical-properties-of-dna-for-reliable-sequence-classification
#12
Artem Ryasik, Mikhail Orlov, Evgenia Zykova, Timofei Ermak, Anatoly Sorokin
Predicting promoter activity of DNA fragment is an important task for computational biology. Approaches using physical properties of DNA to predict bacterial promoters have recently gained a lot of attention. To select an adequate set of physical properties for training a classifier, various characteristics of DNA molecule should be taken into consideration. Here, we present a systematic approach that allows us to select less correlated properties for classification by means of both correlation and cophenetic coefficients as well as concordance matrices...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29375012/assa-fast-identification-of-statistically-significant-interactions-between-long-rnas
#13
Ivan Antonov, Andrey Marakhonov, Maria Zamkova, Yulia Medvedeva
The discovery of thousands of long noncoding RNAs (lncRNAs) in mammals raises a question about their functionality. It has been shown that some of them are involved in post-transcriptional regulation of other RNAs and form inter-molecular duplexes with their targets. Sequence alignment tools have been used for transcriptome-wide prediction of RNA-RNA interactions. However, such approaches have poor prediction accuracy since they ignore RNA's secondary structure. Application of the thermodynamics-based algorithms to long transcripts is not computationally feasible on a large scale...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29361895/identification-of-potential-drug-targets-for-treatment-of-refractory-epilepsy-using-network-pharmacology
#14
Vladislav Bezhentsev, Sergey Ivanov, Sandeep Kumar, Rajesh Goel, Vladimir Poroikov
Epilepsy is the fourth most common neurological disease after migraine, stroke, and Alzheimer's disease. Approximately one-third of all epilepsy cases are refractory to the existing anticonvulsants. Thus, there is an unmet need for newer antiepileptic drugs (AEDs) to manage refractory epilepsy (RE). Discovery of novel AEDs for the treatment of RE further retards for want of potential pharmacological targets, unavailable due to unclear etiology of this disease. In this regard, network pharmacology as an area of bioinformatics is gaining popularity...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29301444/the-search-of-car-ahr-esrs-binding-sites-in-promoters-of-intronic-and-intergenic-micrornas
#15
Vladimir Y Ovchinnikov, Denis V Antonets, Lyudmila F Gulyaeva
MicroRNAs (miRNAs) play important roles in the regulation of gene expression at the post-transcriptional level. Many exogenous compounds or xenobiotics may affect microRNA expression. It is a well-established fact that xenobiotics with planar structure like TCDD, benzo(a)pyrene (BP) can bind aryl hydrocarbon receptor (AhR) followed by its nuclear translocation and transcriptional activation of target genes. Another chemically diverse group of xenobiotics including phenobarbital, DDT, can activate the nuclear receptor CAR and in some cases estrogen receptors ESR1 and ESR2...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29281954/mimpfc-identifying-mirna-mrna-regulatory-modules-by-combining-phase-only-correlation-and-improved-rough-fuzzy-clustering
#16
Dan Luo, Shu-Lin Wang, Jianwen Fang, Wei Zhang
MicroRNAs (miRNAs) play a key role in gene expression and regulation in various organisms. They control a wide range of biological processes and are involved in several types of cancers by causing mRNA degradation or translational inhibition. However, the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With the accumulation of the expression data of miRNAs and mRNAs, many computational methods have been proposed to predict miRNA-mRNA regulatory relationship. However, most existing methods require the number of modules predefined that may be difficult to determine beforehand...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29281953/argo_cuda-exhaustive-gpu-based-approach-for-motif-discovery-in-large-dna-datasets
#17
Oleg V Vishnevsky, Andrey V Bocharnikov, Nikolay A Kolchanov
The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29281952/kernel-differential-subgraph-reveals-dynamic-changes-in-biomolecular-networks
#18
Jiang Xie, Dongfang Lu, Jiaxin Li, Jiao Wang, Yong Zhang, Yanhui Li, Qing Nie
Many major diseases, including various types of cancer, are increasingly threatening human health. However, the mechanisms of the dynamic processes underlying these diseases remain ambiguous. From the holistic perspective of systems science, complex biological networks can reveal biological phenomena. Changes among networks in different states influence the direction of living organisms. The identification of the kernel differential subgraph (KDS) that leads to drastic changes is critical. The existing studies contribute to the identification of a KDS in networks with the same nodes; however, networks in different states involve the disappearance of some nodes or the appearance of some new nodes...
February 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29566637/integrating-virtual-screening-and-biochemical-experimental-approach-to-identify-potential-anti-cancer-agents-from-drug-databank
#19
Suman Jyoti Deka, Ashalata Roy, Debasis Manna, Vishal Trivedi
Chemical libraries constitute a reservoir of pharmacophoric molecules to identify potent anti-cancer agents. Virtual screening of heterocyclic compound library in conjugation with the agonist-competition assay, toxicity-carcinogenicity analysis, and string-based structural searches enabled us to identify several drugs as potential anti-cancer agents targeting protein kinase C (PKC) as a target. Molecular modeling study indicates that Cinnarizine fits well within the PKC C2 domain and exhibits extensive interaction with the protein residues...
January 25, 2018: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/29566636/an-automated-approach-to-classification-of-duplex-assay-for-digital-droplet-pcr
#20
Cong Liu, Wuping Zhou, Tao Zhang, Keming Jiang, Haiwen Li, Wenfei Dong
In the digital polymerase chain reaction (dPCR) detection process, discriminating positive droplets from negative ones directly affects the final concentration and is one of the most important factors affecting accuracy. Current automated classification methods usually discuss single-channel detections, whereas duplex detection experiments are less discussed. In this paper, we designed a classification method by estimating the upper limit of the negative droplets. The right tail of the negative droplets is approximated using a generalized Pareto distribution...
January 25, 2018: Journal of Bioinformatics and Computational Biology
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